Prediction of Coal Ash Fusion Temperature by Least-Squares Support Vector Machine Model
Prediction of Coal Ash Fusion Temperature by Least-Squares Support Vector Machine Model
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DOI：
10.1021/ef100138f
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发表时间：
2010-04
期刊：
影响因子：
5.3
通讯作者：
Bingtao Zhao;Zhong-xiao Zhang;Xiaojiang Wu
中科院分区：
文献类型：
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作者：
Bingtao Zhao;Zhong-xiao Zhang;Xiaojiang Wu
煤灰熔融温度是电厂燃煤锅炉设计和评价的重要参数之一。煤灰的化学成分与煤灰的灰化率之间的关系是相当复杂的，这给煤灰灰化率的建模带来了困难。在这项工作中，最小二乘支持向量机（LS-SVM）模型，这是基于动态优化搜索技术与交叉验证，煤灰软化温度（ST）的预测。通过与不同煤种的实验数据对比，验证了该模型的准确性。此外，本LS-SVM模型与传统模型，例如，多线性回归（MLR）和多非线性模型（MNR）以及人工神经网络（ANN）模型的比较表明，LS-SVM模型是更好地提供最高的广义精度的均方误差为0.0128和相关系数为0.9272。基于LS-SVM模型，...
Coal ash fusion temperature (AFT) is one important parameter for coal-fired boiler design and evaluation in a power plant. The relationship between coal AFT and the chemical composition of coal ash is rather complex in nature and makes the modeling of AFT difficult. In this work, a least-squares support vector machine (LS-SVM) model, which was based on a dynamically optimized search technique with cross-validation, is developed to predict the coal ash softening temperature (ST). The accuracy of this LS-SVM model was verified by comparison with the experimental AFT data of different types of coal. Further, the comparison of the present LS-SVM model and the traditional models, for example, multilinear regressions (MLR) and multi-nonlinear models (MNR) as well as the artificial neural network (ANN) models, showed that the LS-SVM model was much better to provide the highest generalized accuracy with the mean squared error of 0.0128 and correlation coefficient of 0.9272. Furthermore, based on the LS-SVM model,...